Category Growth Model

The next level in the Pricing Tool, is to understand how your pricing level influence the market performance. By replicating some of the skills from high school math; by calculating mean, standard deviation and correlation coefficient, we can can now see how many weeks you contribute to category growth. This is a seamless selection from the Price Blocker, but for good order, we take it step by step also here.

Step 1: Do your selection

At this initial stage, you simply select whatever you would like to analyze (Red), and your corresponding benchmark (Green). As said above; this is a seamless process in the complete model, though you are also able to do it ‘in the model itself’ as you work throgh it. We do call the Selection ‘Product A’, and the Benchmark for ‘Product B’.

In this analysis we select ‘YOU’ versus The Category. We have here selected a very strong brand / pack in a given category.

Step 2: By the numbers
The second step in the Category Growth Model, is a rather busy table that shows the granularity of your performance and the category performance on a weekly level. You do not have to understand the math or the statistics, other than the correlation for your growth and the category growth. The magic number showing number of weeks contributing to growth, is visualized in the bottom right corner. Here you see that the the analyzed product moves according to the market 51 out of 52 weeks. The competing product that the retailer favors in the planogram process, only contributes to growth 24 weeks in a year. That is, regardless of the motives, a lost opportunity to category growth by creating more shopper value to be harvested.

How many weeks do you drive category growth?
By simple measures, we see that ‘our’ product is driving growth 51 weeks in a year. When the model is turned around, we see that the competing product only drives growth 24 weeks. It should be a no-brainer what product the retailers should give preference to.

Step 3: By the plots
The scatter plot is simply a visual display of the table above. What is important to look out for, is how the plots are performing around the function / linear ‘trend’ line. The closer they lay around the line, the higher the likelihood of a product that leads to growth. Here you can see the plots (read: weekly sales at the given price points) have a close distance to the function; except for 3 weeks. We can also simply see what weeks that would come into the ‘negative’ squares. Here only week 50 had a negative effect.

Strong or weak performance?
The closer the distance between the plots and the function, the stronger the product. Out of 52 weeks, only ‘week 50’ had a negative impact on the category growth.

Step 4: Head
As seen in the table above, the bar graph displays number of weeks where growth is created (51 weeks). The middle section shows the ‘elastic effect’, or rather the injection of what the engine of our product can lead to. By simple calculations, we see that ‘our product’ has a 32% bigger effect on creating growth, compared to product A and B in a co-alliance. The ‘truth factor’ is rather high, showing a score of 78%. That is statistically significant. We had only 3 weeks of outliers, which can be looked further into.

How do you perform?
We can easily see that ‘our’ chosen product is an engine for growth, and has a bigger potential performing overall growth for the retailers. That is why hard facts should speak louder than soft smiles.

This leads us over to the next part of the Pricing Tool, where we will look into The Cross Elasticity of Demand.